Retrieval-augmented generation (RAG) offers an effective approach for
addressing question answering (QA) tasks. However, the imperfections of the
retrievers in RAG models often result in the retrieval of irrelevant
information, which could introduce noises and degrade the performance,
especially when handling multi-hop questions that require multiple steps of
reasoning. To enhance the multi-hop reasoning ability of RAG models, we propose
TRACE. TRACE constructs knowledge-grounded reasoning chains, which are a series
of logically connected knowledge triples, to identify and integrate supporting
evidence from the retrieved documents for answering questions. Specifically,
TRACE employs a KG Generator to create a knowledge graph (KG) from the
retrieved documents, and then uses an Autoregressive Reasoning Chain
Constructor to build reasoning chains. Experimental results on three multi-hop
QA datasets show that TRACE achieves an average performance improvement of up
to 14.03% compared to using all the retrieved documents. Moreover, the results
indicate that using reasoning chains as context, rather than the entire
documents, is often sufficient to correctly answer questions.

TRACE 利用知识图谱和推理链的构建提升了多跳推理能力，通过利用检索文档中的支持证据回答问题，平均提升了 14.03% 的性能，并表明仅使用推理链作为上下文通常足以正确回答问题。